Gradient-index optical filter synthesis with controllable and predictable refractive index profiles
Bibliographic record
Abstract
The Fourier transform thin film synthesis method often results in solutions that call for indices that lie outside the range of values of the available materials. To make the resulting refractive index profiles always realizable in our meta-mode sputtering machine, a modified Fourier transform synthesis method is proposed with which the reflectance spectra can be accurately synthesized with controllable and predictable refractive index profiles. In our procedure, an optimal phase function is explored to yield acceptable refractive index profiles. Then the overall thickness is estimated using the Parseval theorem. Finally, several errors inherent to the Fourier transform method, including the imprecision of the spectral function, the truncation of the film and the apodization of the refractive index profiles, are compensated by successive corrections to the magnitude of the spectral function. An explicit iterative formula based on the derivative of the magnitude function is proposed for the compensation of the spectral mismatches. We show with a number of examples that, with the proposed method, it is possible to synthesize gradient-index optical filters with almost any desired spectral performance using experimentally realizable refractive indices.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".